Compared to the panel data setting the problem of change point analysis in functional data
is better behaved due to the additional structure implied
by the assumption of observing a random function
which helps lifting part of the burden of the curse of dimensionality.
Some approaches [1]}, [2]}
to the problem of detecting changes in the mean function
are based on dimension reduction e.g. via functional principal component analysis.
Another line of research proposes
`fully functional' procedures [3]}, [4]}
in the sense that they are not based on dimension reduction.
[5]} propose a compromise between the two approaches
in the context of change point detection in the covariance structure.
